Simulation-Based Inference for Cluster Cosmology with Set-Based Neural Network Architectures

Abstract

The unprecedented statistical power of galaxy cluster catalogs from the SRG (Spectrum Roentgen Gamma)/eROSITA All-Sky Survey provides a unique opportunity to place stringent constraints on cosmological models through measurements of structure growth. Fully exploiting the potential of these large X-ray-selected cluster samples, however, requires robust statistical frameworks that accurately connect observable quantities to the underlying cosmological parameters. We develop and implement a simulation-based inference (SBI) framework for cosmological parameter estimation using a realistic mock-generation pipeline calibrated on eRASS1 simulations. Synthetic galaxy cluster catalogs are propagated through the survey selection function to produce mock eRASS1 observations that reproduce the data's statistical properties. At the core of the method lies a set-based neural network (GNN on sets) that encodes information from individual clusters and is coupled to a masked autoregressive flow for flexible posterior density estimation. This approach enables the use of the full cluster-level information content without compressing the observables into binned summary statistics. Our framework recovers the input cosmologies within the inferred uncertainties, and passes calibration tests, demonstrating robustness in the presence of realistic survey effects. We obtain mock constraints of 11.5% on $Ω_m$ and 4.4% on $σ_8$ averaged over a suite of simulated cluster catalogs matching the effective sample size of the data set (3,259 clusters). We achieve a precision comparable to that obtained with traditional MCMC analyses based on substantially larger cluster samples. The framework is readily extensible to more complex forward models and additional observables. This work highlights the potential of SBI methods for next-generation large-scale structure analyses with forthcoming X-ray cluster surveys.